Sarkar - TEXT ANALYTICS WITH PYTHON: a practical real-world approach to gaining actionable insights from ... your data

Any source code or other supplementary material referenced by the author in this book is available to readers on GitHub via the books product page, located at www.apress.com/978-1-4842-4353-4 . For more detailed information, please visit http://www.apress.com/source-code .

The power of text analytics and natural language processing is beginning to live up to its promise, thanks to contemporary developments in machine learning.

If you have read Dipanjan Sarkars Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data , then you probably already have some sense that this is true. Released in 2016, this book has quickly become a staple in the natural language processing community. Yet, in the world of technology, 2 years can seem like a lifetime, and so welcome to the updated second edition of Text Analytics with Python !

While the core of the first editions original material has been preserved, there are a number of updates and changes throughout. Of note, text classification and sentiment analysis have been expanded to include both traditional machine learning and deep learning models, important as neural networks become increasingly central in approaches to natural language processing. Additionally, topic modeling, a collection of techniques for abstract topic discovery, has been further developed to include a number of complementary methods, and to leverage additional Python libraries.

There is also an entire new chapter on feature engineering which plays an especially central role in natural language processing and text data where both traditional and neural network-based methods are covered. In addition, as much as deep learning is discussed in terms of natural language processing these days, there is a palpable sense that it is only the beginning; to that end, an entire new chapter is dedicated to the promise of deep learning for natural language processing.

Data is the new oil and unstructured dataespecially text, images, and videoscontains a wealth of information. However, due to the inherent complexity in processing and analyzing this data, people often refrain from spending extra time and effort venturing out from structured datasets to analyze these unstructured sources of data, which can be a potential gold mine. Natural language processing (NLP) is all about leveraging tools, techniques, and algorithms to process and understand natural language-based data, which is usually unstructured like text, speech, and so on. In this book, we will be looking at tried and tested strategiestechniques and workflowsthat can be leveraged by practitioners and data scientists to extract useful insights from text data.

Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity expected of any data scientist in todays fast-paced world! Text Analytics with Python is a practitioners guide to learning and applying NLP techniques to extract actionable insights from noisy and unstructured text data. This book helps its readers understand essential concepts in NLP along with extensive case studies and hands-on examples to master state-of-the-art tools, techniques, and frameworks for actually applying NLP to solve real-world problems. We leverage Python 3 and the latest and best state-of-the-art frameworks, including NLTK, Gensim, spaCy, Scikit-Learn, TextBlob, Keras, and TensorFlow, to showcase the examples in the book. You can find all the examples used in the book on GitHub at https://github.com/dipanjanS/text-analytics-with-python .

In my journey in this field so far, I have struggled with various problems, faced many challenges, and learned various lessons over time. This book contains a major chunk of the knowledge Ive gained in the world of text analytics and natural language processing, where building a fancy word cloud from a bunch of text documents is not enough anymore. Perhaps the biggest problem with regard to learning text analytics is not a lack of information but too much information, often called information overload . There are so many resources, documentation, papers, books, and journals containing so much content that they often overwhelm someone new to the field. You might have had questions like, What is the right technique to solve a problem?, How does text summarization really work?, and Which frameworks are best for solving multi-class text categorization?, among many others! By combining mathematical and theoretical concepts with practical implementations of real-world case studies using Python, this book tries to address this problem and help readers avoid the pressing issues Ive faced in my journey so far.